Artificial intelligence technology in aortic valve disease: a decade of scientometric and narrative review
By
Peng Hei
He Ren
Wenshuai Ma
Wei Fang
Yan Li
July 9, 2026
Clinical Scorecard: A Decade of Research on Artificial Intelligence Applications in Aortic Valve Disease: A Scientometric and Narrative Overview
At a Glance
Category Detail
Condition Aortic Valve Disease Key Mechanisms Artificial intelligence technology for diagnosis, risk stratification, and prognosis prediction. Target Population Elderly individuals, particularly those aged ≥65 years. Care Setting Clinical settings utilizing imaging assessments and AI technologies.
Key Highlights
AI-assisted diagnosis and risk stratification are key research hotspots. The United States leads in research output and influence. Deep learning and machine learning techniques are primarily used. Mayo Clinic identified as the most prolific institution in this field. Keyword clustering reveals themes in disease diagnosis and clinical outcomes. Guideline-Based Recommendations
Diagnosis
Utilize AI technologies for automated valve calcification assessment and regurgitation quantification. Management
Incorporate AI in optimizing risk models based on clinical data. Monitoring & Follow-up
Develop multimodal models to enhance patient lifecycle management. Risks
Challenges include identifying early asymptomatic stages and predicting disease progression. Patient & Prescribing Data
Elderly patients with aortic valve disease, particularly aortic stenosis.
AI technologies can improve diagnostic accuracy and efficiency in clinical decision-making.
Clinical Best Practices
Employ AI for enhanced electrocardiography and echocardiography in valve disease detection. Focus on integrating AI tools into clinical workflows for better patient outcomes. Related Resources & Content
